[Correlation between arousal threshold and occurrence of hypertension in patients with obstructive sleep apnea hypopnea syndrome].

Objective: To investigate the relationship between arousal threshold (ArTH) and hypertension in patients with obstructive sleep apnea hypopnea syndrome (OSA). Methods: This study recruited 648 patients diagnosed with OSA at the Sleep Center of the Second Affiliated Hospital of Soochow University from January 2020 to August 2021, including 569 males and 79 females, aged 42(35,52) years. The basic demographic information and clinical data of all patients were collected, including blood pressure measurement, and relevant questionnaire scores, and nocturnal polysomnography (PSG) parameters. A clinical predictive model based on sleep apnea hypopnea index (AHI), lowest pulse oxygen saturation (LSpaO2) and hypopnea ratio (FHypopneas) was used to access the arousal threshold of OSA patients. Patients were divided into OSA group and OSA with hypertension group according to whether they were combined with hypertension. The differences in the above indexes between the two groups were analyzed to explore the relationship between arousal threshold and hypertension in OSA patients, using a binary logistic stepwise regression analysis. Results: A total of 648 OSA patients were enrolled, including 415 in the OSA with hypertension group and 233 in the OSA group. Compared with OSA group, OSA with hypertension group had older age, higher body mass index (BMI), higher blood pressure at bedtime and at awakening, higher AHI and lower proportion of hypopnea (all P<0.05). There were no significant differences between other general data and PSG parameters (all P>0.05). The proportion of patients with low arousal threshold (AHI<30 events per hour, LSpO2>82.5%, Fhypopneas>58.3%) in OSA with hypertension group was lower, and the proportion of phenotypic patients with low arousal threshold was significantly lower (30.1% vs. 52.4% P<0.001). Binary logistic stepwise regression analysis showed that the high arousal threshold (OR=1.930, 95%CI:1.326-2.808, P=0.001) was an independent risk factor for OSA complicated with hypertension. Conclusion: The arousal threshold is associated with the development of hypertension in OSA patients, and OSA patients with a high arousal threshold have a higher risk of developing hypertension.

Association between obstructive sleep apnea and arrhythmia and heart rate variability among hypertensive patients.

BACKGROUND: The relationship between obstructive sleep apnea (OSA) and the occurrence of arrhythmias and heart rate variability (HRV) in hypertensive patients is not elucidated. Our study investigates the association between OSA, arrhythmias, and HRV in hypertensive patients. METHODS: We conducted a cross-sectional analysis involving hypertensive patients divided based on their apnea-hypopnea index (AHI) into two groups: the AHI ≤ 15 and the AHI > 15. All participants underwent polysomnography (PSG), 24-hour dynamic electrocardiography (DCG), cardiac Doppler ultrasound, and other relevant evaluations. RESULTS: The AHI > 15 group showed a significantly higher prevalence of frequent atrial premature beats and atrial tachycardia (P = 0.030 and P = 0.035, respectively) than the AHI ≤ 15 group. Time-domain analysis indicated that the standard deviation of normal-to-normal R-R intervals (SDNN) and the standard deviation of every 5-minute normal-to-normal R-R intervals (SDANN) were significantly higher in the AHI > 15 group (P = 0.020 and P = 0.033, respectively). Frequency domain analysis revealed that the low-frequency (LF), high-frequency (HF) components, and the LF/HF ratio were also significantly elevated in the AHI > 15 group (P < 0.001, P = 0.031, and P = 0.028, respectively). Furthermore, left atrial diameter (LAD) was significantly larger in the AHI > 15 group (P < 0.001). Both univariate and multivariable linear regression analyses confirmed a significant association between PSG-derived independent variables and the dependent HRV parameters SDNN, LF, and LF/HF ratio (F = 8.929, P < 0.001; F = 14.832, P < 0.001; F = 5.917, P = 0.016, respectively). CONCLUSIONS: Hypertensive patients with AHI > 15 are at an increased risk for atrial arrhythmias and left atrial dilation, with HRV significantly correlating with OSA severity.

[Idiopathic hypersomnia: a kind of sleep disorder that cannot be ignored].

Idiopathic hypersomnia(IH) is a chronic central disorders of hypersomnolence that manifests as excessive daytime sleepiness occurring despite normal or prolonged sleep time. Due to the individual heterogeneity of disease, the high overlap of clinical, poor repeatability of polysomnography monitoring results and the lack of clear disease biomarkers, clinical diagnosis and differential diagnosis are still difficult. This article summarizes the update of diagnostic criteria, clinical manifestations, diagnosis and treatment strategies of IH, in order to receive attention, increase the recognition rate of clinical diagnosis, reduce the misdiagnosis rate and missed diagnosis rate.

[The relationship between rapid eye movement electromyogram activity and sleep stability in rapid eye movement sleep behavior disorder].

Objective: To investigate the relationship between the types of electromyogram (EMG) activity and sleep stability during rapid eye movement (REM) in patients with rapid eye movement sleep behavior disorder(RBD). Methods: One hundred and three patients with RBD who met the inclusion and exclusion criteria at the Second Affiliated Hospital of Air Force Military Medical University from January 2017 to December 2019 were retrospectively analyzed. The general situation, clinical symptoms, sleep and emotion questionnaires and nocturnal PSG data were collected. According to the different proportions of tonic and phasic EMG activity, the group with a higher proportion of tonic EMG than phasic EMG was defined as the tonic dominant group, and the group with a higher proportion of phasic EMG than tonic was defined as the phasic dominant group. The sleep instability index was calculated according to the ratio of the number of transitions from sleep to wakefulness to the total sleep time of each sleep stage. Multiple linear regression was used to explore the relationship between REM EMG activity and sleep instability index. Results: A total of 35 idiopathic RBD (iRBD) patients were included, aged(54.5±18.2)years, with 17 males and 18 females. There were 27 RBD with Parkinson's disease (PD), with an average age of (59.4±7.9)years, including 17 males and 10 females. Additionally, there were 41 RBD patients with narcolepsy, aged (21.2±13.2)years, consisting of 22 males and 19 females. Both iRBD and RBD patients with PD had lower objective total sleep time, sleep latency, sleep efficiency, wake time after sleep onset and the percentage of N3 sleep compared to RBD with episodic sleep disorder (all P<0.05). N1-W index[M(Q1, Q3),10.6 (6.5, 16.9)/h vs 7.3 (4.7, 10.5)/h], N2-W index [4.0 (2.2, 5.6)/h vs 2.3 (1.5, 3.9)/h], NREM-W index [ (5.8±2.9)/h vs (4.5±3.2)/h] and REM-W index[ 3.9 (1.9, 7.3)/h vs 2.7 (1.0, 4.0)/h] in the phasic dominant group were higher than those in the tonic dominant group. After adjusting for confounding factors, the effect of phasic EMG dominant group on REM-W was higher than that in the tonic dominant group (ß=2.05, 95%CI: 0.09-3.26, P=0.012). Conclusion: In RBD patients, the phasic EMG activity has a significant impact on sleep stability, especially on REM sleep stability.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep.

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.

Melatonin mediates intestinal barrier dysfunction and systemic inflammation in moderate-severe OSA patients.

BACKGROUND: Intestinal barrier dysfunction and systemic inflammation are common in obstructive sleep apnoea (OSA). We aimed to investigate the role of melatonin, an anti-inflammatory mediator, in mediating the relationships between OSA, intestinal barrier dysfunction and systemic inflammation. METHODS: Two hundred and thirty-five male participants who complained with sleep problems and underwent whole night polysomnography at our sleep centre between 2017 and 2018 were enrolled. Polysomnographic data, anthropometric measurements and biochemical indicators were collected. Serum melatonin, intestinal barrier function biomarker zonula occludens-1 (ZO-1) and inflammatory biomarkers C-reactive protein (CRP) with lipopolysaccharide (LPS) were detected. Spearman's correlation analysis assessed the correlations between sleep parameters, melatonin and biomarkers (ZO-1, LPS and CRP). Mediation analysis explored the effect of OSA on intestinal barrier dysfunction and systemic inflammation in moderate-severe OSA patients. RESULTS: As OSA severity increased, serum melatonin decreased, whereas ZO-1, LPS and CRP increased. Spearman's correlation analysis showed that serum melatonin was significantly negatively correlated with ZO-1 (r = -0.19, p < .05) and LPS (r = -0.20, p < .05) in the moderate-OSA group; serum melatonin was significantly negatively correlated with ZO-1 (r = -0.46, p < .01), LPS (r = -0.35, p < .01) and CPR (r = -0.30, p < .05) in the severe-OSA group. Mediation analyses showed melatonin explain 36.12% and 35.38% of the effect of apnoea-hypopnea index (AHI) on ZO-1 and LPS in moderate to severe OSA patients. CONCLUSIONS: Our study revealed that melatonin may be involved in mediating intestinal barrier dysfunction and systemic inflammation in moderate-to-severe OSA patients.

Obstructive Sleep Apnea in Adults: Common Questions and Answers.

Obstructive sleep apnea (OSA) is a common disorder that affects quality of life and is associated with comorbidities such as hypertension, atrial fibrillation, heart failure, coronary heart disease, type 2 diabetes mellitus, and stroke. OSA is characterized by a reduction or cessation of breathing during sleep, resulting in intermittent hypoxemia, autonomic fluctuation, and sleep fragmentation. The U.S. Preventive Services Task Force states that there is insufficient evidence to recommend routine screening for OSA in the absence of symptoms. OSA should be considered in patients with excessive daytime fatigue, unrestful sleep, persistent snoring, and nocturnal awakenings with gasping or choking. The STOP-BANG questionnaire is the most sensitive screening tool for OSA, and the diagnostic standard is polysomnography with an observed apnea-hypopnea index greater than 5 in the presence of symptoms or greater than 15 without symptoms. Home sleep apnea testing is a useful diagnostic option in patients who have symptoms consistent with moderate to severe OSA without significant cardiopulmonary comorbidities. Positive airway pressure, with a humidified nasal or facial mask, is the first-line treatment for adults with OSA. Weight loss is a beneficial adjunct to treatment through intensive lifestyle modification, medications, or bariatric surgery. Alternatives for patients intolerant of or nonadherent to positive airway pressure include changing the type of mask used, mandibular advancement devices, hypoglossal nerve stimulation, and other surgical interventions. Although many OSA therapies effectively improve daytime sleepiness and blood pressure, none have demonstrated a mortality benefit in randomized controlled trials.

Sleep architecture in idiopathic hypersomnia: the influence of age, sex, and body mass index.

This study aimed to progress the understanding of idiopathic hypersomnia (IH) by assessing the moderating influence of individual characteristics, such as age, sex, and body mass index (BMI) on sleep architecture. In this retrospective study, 76 IH participants (38.1 ± 11.3 years; 40 women) underwent a clinical interview, an in-laboratory polysomnography with a maximal 9-h time in bed and a multiple sleep latency test (MSLT). They were compared to 106 healthy controls (38.1 ± 14.1 years; 60 women). Multiple regressions were used to assess moderating influence of age, sex, and BMI on sleep variables. We used correlations to assess whether sleep variables were associated with Epworth Sleepiness Scale scores and mean sleep onset latency on the MSLT in IH participants. Compared to controls, IH participants had shorter sleep latency (p = 0.002), longer total sleep time (p < 0.001), more time spent in N2 sleep (p = 0.008), and showed trends for a higher sleep efficiency (p = 0.023) and more time spent in rapid eye movement (REM) sleep (p = 0.022). No significant moderating influence of age, sex, or BMI was found. More severe self-reported sleepiness in IH patients was correlated with shorter REM sleep latency and less N1 sleep in terms of proportion and duration (ps < 0.01). This study shows that, when compared to healthy controls, patients with IH had no anomalies in their sleep architecture that can explain their excessive daytime sleepiness. Moreover, there is no moderating influence of age, sex, and BMI, suggesting that the absence of major group differences is relatively robust.

Molecular characterization of the circadian clock in patients with Parkinson's disease-CLOCK4PD Study protocol.

INTRODUCTION: Circadian rhythms (CRs) orchestrate intrinsic 24-hour oscillations which synchronize an organism's physiology and behaviour with respect to daily cycles. CR disruptions have been linked to Parkinson's Disease (PD), the second most prevalent neurodegenerative disorder globally, and are associated to several PD-symptoms such as sleep disturbances. Studying molecular changes of CR offers a potential avenue for unravelling novel insights into the PD progression, symptoms, and can be further used for optimization of treatment strategies. Yet, a comprehensive characterization of the alterations at the molecular expression level for core-clock and clock-controlled genes in PD is still missing. METHODS AND ANALYSIS: The proposed study protocol will be used to characterize expression profiles of circadian genes obtained from saliva samples in PD patients and controls. For this purpose, 20 healthy controls and 70 PD patients will be recruited. Data from clinical assessment, questionnaires, actigraphy tracking and polysomnography will be collected and clinical evaluations will be repeated as a follow-up in one-year time. We plan to carry out sub-group analyses considering several clinical factors (e.g., biological sex, treatment dosages, or fluctuation of symptoms), and to correlate reflected changes in CR of measured genes with distinct PD phenotypes (diffuse malignant and mild/motor-predominant). Additionally, using NanoStringⓇ multiplex technology on a subset of samples, we aim to further explore potential CR alterations in hundreds of genes involved in neuropathology pathways. DISCUSSION: CLOCK4PD is a mono-centric, non-interventional observational study aiming at the molecular characterization of CR alterations in PD. We further plan to determine physiological modifications in sleep and activity patterns, and clinical factors correlating with the observed CR changes. Our study may provide valuable insights into the intricate interplay between CR and PD with a potential to be used as a predictor of circadian alterations reflecting distinct disease phenotypes, symptoms, and progression outcomes.

Cardio-metabolic health effects of CPAP treatment for sleep apnoea during weight loss: A randomised controlled pilot trial.

BACKGROUND AND AIMS: This study assessed whether the addition of continuous positive airway pressure (CPAP) during weight loss would enhance cardiometabolic health improvements in patients with obesity and Obstructive Sleep Apnoea (OSA). METHODS AND RESULTS: Patients with overweight or obesity, pre-diabetes and moderatesevere OSA were randomised to receive CPAP therapy with a weight loss programme (CPAP+WL) or a weight loss programme alone (WL alone). PRIMARY OUTCOME: 2-hour glucose assessed by an oral glucose tolerance test. SECONDARY OUTCOMES: 24 hr blood pressure, body composition (DEXA) and fasting blood markers. 17 patients completed 3-month follow-up assessments (8 CPAP+WL and 9 WL alone). Overall, participants in both groups lost ∼12 kg which reduced polysomnography determined OSA severity by ∼45 %. In the CPAP+WL group, CPAP use (compliance 5.29 hrs/night) did not improve any outcome above WL alone. There was no improvement in 2-hour glucose in either group. However, in the pooled (n = 17) analysis there were overall improvements in most outcomes including insulin sensitivity (.000965 units, p = .008), sleep systolic BP (- 16.2 mmHg, p = .0003), sleep diastolic BP (-9.8 mmHg, p = 0.02), wake diastolic BP (- 4.3 mmHg, p = .03) and sleepiness (Epworth Sleepiness Score -3.2, p = .0003). In addition, there were reductions in glucose area under the curve (-230 units, p = .009), total (-0.86 mmol/L, p = 0.006) and LDL cholesterol (-0.58 mmol/L, p = 0.007), triglycerides (-0.75 mmol/L, p = 0.004), fat mass (-7.6 kg, p < .0001) and abdominal fat (-310 cm3, p < .0001). CONCLUSION: Weight loss reduced OSA and improved sleepiness and cardiometabolic health. These improvements were not further enhanced by using CPAP. Results suggest weight loss should be the primary focus of treatment for patients with OSA and obesity.

State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.

Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was trained and evaluated on 3423 polysomnograms of people with various sleep disorders. The model architecture is a U-net that accepts 50 Hz signals as input. We compared this algorithm with models trained on publicly available datasets, and evaluated these models using our clinical dataset, particularly with regard to the effects of different sleep disorders. In an effort to evaluate clinical relevance, we designed a metric based on the error of the predicted arousal index. Our models achieve an area under the precision recall curve (AUPRC) of up to 0.83 and F1 scores of up to 0.81. The model trained on our data showed no age or gender bias and no significant negative effect regarding sleep disorders on model performance compared to healthy sleep. In contrast, models trained on public datasets showed a small to moderate negative effect (calculated using Cohen's d) of sleep disorders on model performance. Therefore, we conclude that state-of-the-art arousal detection on our clinical data is possible with our model architecture. Thus, our results support the general recommendation to use a clinical dataset for training if the model is to be applied to clinical data.

Sleep, psychological health, and physical activity level in patients with hypertension.

The aim of this study was to compare sleep, daytime sleepiness, and psychological health in physically active versus inactive patients with hypertension. A cross-sectional design included thirty-seven participants (ACTIVE, n = 15; INACTIVE, n = 22). Sleep was assessed by polysomnography, the Pittsburgh Sleep Quality Index (PSQI) and a one-week daily sleep diary. The sleepiness was assessed with the Epworth Sleepiness Scale and the psychological health was assessed with the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI) and Profile of Mood States (POMS). Habitual physical activity was assessed with 7 day-step counts recorded by a pedometer and questionnaire. Significantly lower PSQI score (mean ± S.D.; 7.3 ± 3.4 vs 10.1 ± 3.6) and daytime sleepiness (8.7 ± 4.5 vs. 11.9 ± 4.4) were found in the physically active versus inactive participants, respectively. In addition, higher PSQI-total sleep time (6.9 ± 1.3 vs 5.6 ± 1.1) and vigor/activity (19.7 ± 3.9 vs 16.0 ± 3.9), and lower depressed mood on the POMS scale (8.2 ± 7.9 vs 13.8 ± 10.0) and lower POMS total mood disturbance (21.0 ± 27.0 vs 43.5 ± 32.5) were observed in the active participants compared with the inactive participants. Combining data across both groups, leisure time sport participation correlated negatively with PSQI (r = -0.35; p < 0.05) and BDI (r = -0.42; p < 0.05), and positively with POMS-vigor/activity (r = 0.43; p < 0.05). The results showed regular physical activity was associated with better sleep and psychological health in patients with hypertension.

Association between sleep state misperception and bedtime behavior in patients with chronic insomnia.

Previous studies on sleep state misperception have objectively evaluated sleep status in special environments using polysomnography. There is a paucity of data from studies that evaluated habitual sleep status in home environments. The present study aimed to investigate sleep state misperception in the home environment of patients with chronic insomnia using a lumbar-worn actigraphy to identify sleep habits associated with sleep state misperception severity. Thirty-one patients and 42 healthy volunteers were included in the insomnia and non-insomnia group, respectively. Participants recorded subjective assessments in sleep diaries, objective assessments with an actigraphy worn for 14 days, and self-assessments using questionnaires. Both groups had similar objective sleep ratings; however, insomnia group had significantly worse subjective ratings (total sleep time, wake after sleep onset, and sleep onset latency). A significant correlation was found between subjective and objective total sleep time scores in non-insomnia group but not in insomnia group. Insomnia group had earlier bedtimes, significantly longer bedtimes, and impaired daytime functioning (Sheehan Disability Scale score); additionally, they underestimated their total sleep time, particularly with earlier bedtimes and longer laying durations. Monitoring the sleep status and habits of individuals in home environments could be instrumental in identifying key points for targeted interventions on sleep hygiene and cognitive behavioral therapy for insomnia.

Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications.

Change-point detection using diffusion maps for sleep apnea monitoring with contact-free sensors.

Monitoring and improving the quality of sleep are crucial from a public health perspective. In this study, we propose a change-point detection method using diffusion maps for a more accurate detection of respiratory arrest points. Conventional change-point detection methods are limited when dealing with complex nonlinear data structures, and the proposed method overcomes these limitations. The proposed method embeds subsequence data in a low-dimensional space while considering the global and local structures of the data and uses the distance between the data as the score of the change point. Experiments using synthetic and real-world contact-free sensor data confirmed the superiority of the proposed method when dealing with noise, and it detected apnea events with greater accuracy than conventional methods. In addition to improving sleep monitoring, the proposed method can be applied in other fields, such as healthcare, manufacturing, and finance. This study will contribute to the development of advanced monitoring systems that adapt to diverse conditions while protecting privacy.

Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?

Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.

First-night effect reduces the beneficial effects of sleep on visual plasticity and modifies the underlying neurochemical processes.

Individuals experience difficulty falling asleep in a new environment, termed the first night effect (FNE). However, the impact of the FNE on sleep-induced brain plasticity remains unclear. Here, using a within-subject design, we found that the FNE significantly reduces visual plasticity during sleep in young adults. Sleep-onset latency (SOL), an indicator of the FNE, was significantly longer during the first sleep session than the second session, confirming the FNE. We assessed performance gains in visual perceptual learning after sleep and increases in the excitatory-to-inhibitory neurotransmitter (E/I) ratio in early visual areas during sleep using magnetic resonance spectroscopy and polysomnography. These parameters were significantly smaller in sleep with the FNE than in sleep without the FNE; however, these parameters were not correlated with SOL. These results suggest that while the neural mechanisms of the FNE and brain plasticity are independent, sleep disturbances temporarily block the neurochemical process fundamental for brain plasticity.